AI RESEARCH
BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search
arXiv CS.AI
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ArXi:2601.11037v2 Announce Type: replace RL-based agentic search enables LLMs to solve complex questions via dynamic planning and external search. While this approach significantly enhances accuracy with agent policies optimized via large-scale reinforcement learning, we identify a critical gap in reliability: these agents fail to recognize their reasoning boundaries and rarely admit ``I DON'T KNOW'' (IDK) even when evidence is insufficient or reasoning reaches its limit. The lack of reliability often leads to plausible but unreliable answers.